OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Detection of Injection and Press Mold Parts on 2D Drawing Using Deep Neural Network
Venue: arXiv cs.CV 2021
Year: 2021

This paper proposes a method to automatically detect the key feature parts in a CAD of commercial TV and monitor using a deep neural network. We developed a deep learning pipeline that can detect the injection parts such as hook, boss, undercut and press parts such as DPS, Embo-Screwless, Embo-Burring, and EMBO in the 2D CAD drawing images. We first cropped the drawing to a specific size for the training efficiency of a deep neural network. Then, we use Cascade R-CNN to find the position of injection and press parts and use Resnet-50 to predict the orientation of the parts. Finally, we convert the position of the parts found through the cropped image to the position of the original image. As a result, we obtained detection accuracy of injection and press parts with 84.1% in AP (Average Precision), 91.2% in AR(Average Recall), 72.0% in AP, 87.0% in AR, and orientation accuracy of injection and press parts with 94.4% and 92.0%, which can facilitate the faster design in industrial product design.
Multimodal Semi-Supervised Learning for 3D Objects
Venue: arXiv cs.CV 2021
Year: 2021

In recent years, semi-supervised learning has been widely explored and shows excellent data efficiency for 2D data. There is an emerging need to improve data efficiency for 3D tasks due to the scarcity of labeled 3D data. This paper explores how the coherence of different modelities of 3D data (e.g. point cloud, image, and mesh) can be used to improve data efficiency for both 3D classification and retrieval tasks. We propose a novel multimodal semi-supervised learning framework by introducing instance-level consistency constraint and a novel multimodal contrastive prototype (M2CP) loss. The instance-level consistency enforces the network to generate consistent representations for multimodal data of the same object regardless of its modality. The M2CP maintains a multimodal prototype for each class and learns features with small intra-class variations by minimizing the feature distance of each object to its prototype while maximizing the distance to the others. Our proposed framework significantly outperforms all the state-of-the-art counterparts for both classification and retrieval tasks by a large margin on the modelNet10 and ModelNet40 datasets.
DistFL: Distribution-aware Federated Learning for Mobile Scenarios
Venue: arXiv cs.LG 2021
Year: 2021

Federated learning (FL) has emerged as an effective solution to decentralized and privacy-preserving machine learning for mobile clients. While traditional FL has demonstrated its superiority, it ignores the non-iid (independently identically distributed) situation, which widely exists in mobile scenarios. Failing to handle non-iid situations could cause problems such as performance decreasing and possible attacks. Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models. However, previous techniques overlook the root causes for the "symptoms": blindly aggregating models with the non-iid distributions. In this paper, we try to fundamentally address the issue by decomposing the overall non-iid situation into several iid clusters and conducting aggregation in each cluster. Specifically, we propose \textbf{DistFL}, a novel framework to achieve automated and accurate \textbf{Dist}ribution-aware \textbf{F}ederated \textbf{L}earning in a cost-efficient way. DistFL achieves clustering via extracting and comparing the \textit{distribution knowledge} from the uploaded models. With this framework, we are able to generate multiple personalized models with distinctive distributions and assign them to the corresponding clients. Extensive experiments on mobile scenarios with popular model architectures have demonstrated the effectiveness of DistFL.
Mechanistic Interpretation of Machine Learning Inference: A Fuzzy Feature Importance Fusion Approach
Venue: arXiv cs.LG 2021
Year: 2021

With the widespread use of machine learning to support decision-making, it is increasingly important to verify and understand the reasons why a particular output is produced. Although post-training feature importance approaches assist this interpretation, there is an overall lack of consensus regarding how feature importance should be quantified, making explanations of model predictions unreliable. In addition, many of these explanations depend on the specific machine learning approach employed and on the subset of data used when calculating feature importance. A possible solution to improve the reliability of explanations is to combine results from multiple feature importance quantifiers from different machine learning approaches coupled with re-sampling. Current state-of-the-art ensemble feature importance fusion uses crisp techniques to fuse results from different approaches. There is, however, significant loss of information as these approaches are not context-aware and reduce several quantifiers to a single crisp output. More importantly, their representation of 'importance' as coefficients is misleading and incomprehensible to end-users and decision makers. Here we show how the use of fuzzy data fusion methods can overcome some of the important limitations of crisp fusion methods.
Findings of the 2nd Photonics and Electronics Technology for Extreme Scale Computing Workgroup (REPETE): Design Challenges for Socket Level Photonic I/O
Venue: arXiv cs.OH 2021
Year: 2021

To inform research activities in HPC interconnect of strategic importance to the USG beyond 2018, in January of 2018, the DoD sponsored the 2nd Photonics and Electronics Technology for Extreme-scale Computing (REPETE) workgroup. REPETE investigated new challenges in the area of HPC interconnect inspired by technology trends and challenges of vital importance to USG stakeholders. The REPETE Working group investigated two focus areas of interest to the USG: Socket Level Photonic IO and Cryogenic Photonic IO. The working group spanned industry, academia, and government, in research, development, product, and technology investment areas. The workgroup team focusing on current and future design challenges for socket level photonic IO began discussing technical challenges and current state of applying photonics to off-chip IO in April of 2019 through biweekly meetings that concluded in early September of 2019. The focus of these meetings was to discuss what technology exists for moving the conversion of electrical signaling to photonic signaling from the node (as is currently done) down to within the socket for off-chip IO. Areas discussed include: I/O Requirements and Trends at the Compute Socket for 2025 and Beyond; Current and Near-Term Copper Solutions for Off-Chip, Socket-Level Interconnect; Current Photonic Solutions for Off-Chip or Socket-Level Interconnect; Light Generation for Off-Chip, Socket-Level IO; Fabrication and Packaging of Photonic Integrated Circuits; All-Photonic Switching Technology; and Simulation of Photonic Interconnects.
Game Redesign in No-regret Game Playing
Venue: arXiv cs.GT 2021
Year: 2021

We study the game redesign problem in which an external designer has the ability to change the payoff function in each round, but incurs a design cost for deviating from the original game. The players apply no-regret learning algorithms to repeatedly play the changed games with limited feedback. The goals of the designer are to (i) incentivize all players to take a specific target action profile frequently; and (ii) incur small cumulative design cost. We present game redesign algorithms with the guarantee that the target action profile is played in T-o(T) rounds while incurring only o(T) cumulative design cost. Game redesign describes both positive and negative applications: a benevolent designer who incentivizes players to take a target action profile with better social welfare compared to the solution of the original game, or a malicious attacker whose target action profile benefits themselves but not the players. Simulations on four classic games confirm the effectiveness of our proposed redesign algorithms.
SwiftLane: Towards Fast and Efficient Lane Detection
Venue: arXiv cs.CV 2021
Year: 2021

Recent work done on lane detection has been able to detect lanes accurately in complex scenarios, yet many fail to deliver real-time performance specifically with limited computational resources. In this work, we propose SwiftLane: a simple and light-weight, end-to-end deep learning based framework, coupled with the row-wise classification formulation for fast and efficient lane detection. This framework is supplemented with a false positive suppression algorithm and a curve fitting technique to further increase the accuracy. Our method achieves an inference speed of 411 frames per second, surpassing state-of-the-art in terms of speed while achieving comparable results in terms of accuracy on the popular CULane benchmark dataset. In addition, our proposed framework together with TensorRT optimization facilitates real-time lane detection on a Nvidia Jetson AGX Xavier as an embedded system while achieving a high inference speed of 56 frames per second.
Automatic Guide Generation for Stan via NumPyro
Venue: arXiv cs.PL 2021
Year: 2021

Stan is a very popular probabilistic language with a state-of-the-art HMC sampler but it only offers a limited choice of algorithms for black-box variational inference. In this paper, we show that using our recently proposed compiler from Stan to Pyro, Stan users can easily try the set of algorithms implemented in Pyro for black-box variational inference. We evaluate our approach on PosteriorDB, a database of Stan models with corresponding data and reference posterior samples. Results show that the eight algorithms available in Pyro offer a range of possible compromises between complexity and accuracy. This paper illustrates that compiling Stan to another probabilistic language can be used to leverage new features for Stan users, and give access to a large set of examples for language developers who implement these new features.
Lightweight Decoding Strategies for Increasing Specificity
Venue: arXiv cs.CL 2021
Year: 2021

Language models are known to produce vague and generic outputs. We propose two unsupervised decoding strategies based on either word-frequency or point-wise mutual information to increase the specificity of any model that outputs a probability distribution over its vocabulary at generation time. We test the strategies in a prompt completion task; with human evaluations, we find that both strategies increase the specificity of outputs with only modest decreases in sensibility. We also briefly present a summarization use case, where these strategies can produce more specific summaries.
Simple Dialogue System with AUDITED
Venue: arXiv cs.CV 2021
Year: 2021

We devise a multimodal conversation system for dialogue utterances composed of text, image or both modalities. We leverage Auxiliary UnsuperviseD vIsual and TExtual Data (AUDITED). To improve the performance of text-based task, we utilize translations of target sentences from English to French to form the assisted supervision. For the image-based task, we employ the DeepFashion dataset in which we seek nearest neighbor images of positive and negative target images of the MMD data. These nearest neighbors form the nearest neighbor embedding providing an external context for target images. We form two methods to create neighbor embedding vectors, namely Neighbor Embedding by Hard Assignment (NEHA) and Neighbor Embedding by Soft Assignment (NESA) which generate context subspaces per target image. Subsequently, these subspaces are learnt by our pipeline as a context for the target data. We also propose a discriminator which switches between the image- and text-based tasks. We show improvements over baselines on the large-scale Multimodal Dialogue Dataset (MMD) and SIMMC.
Self-supervised denoising for massive noisy images
Venue: arXiv cs.CV 2021
Year: 2021

We propose an effective deep learning model for signal reconstruction, which requires no signal prior, no noise model calibration, and no clean samples. This model only assumes that the noise is independent of the measurement and that the true signals share the same structured information. We demonstrate its performance on a variety of real-world applications, from sub-Ångström resolution atomic images to sub-arcsecond resolution astronomy images.
Cleaning Dirty Books: Post-OCR Processing for Previously Scanned Texts
Venue: arXiv cs.CL 2021
Year: 2021

Substantial amounts of work are required to clean large collections of digitized books for NLP analysis, both because of the presence of errors in the scanned text and the presence of duplicate volumes in the corpora. In this paper, we consider the issue of deduplication in the presence of optical character recognition (OCR) errors. We present methods to handle these errors, evaluated on a collection of 19,347 texts from the Project Gutenberg dataset and 96,635 texts from the HathiTrust Library. We demonstrate that improvements in language models now enable the detection and correction of OCR errors without consideration of the scanning image itself. The inconsistencies found by aligning pairs of scans of the same underlying work provides training data to build models for detecting and correcting errors. We identify the canonical version for each of 17,136 repeatedly-scanned books from 58,808 scans. Finally, we investigate methods to detect and correct errors in single-copy texts. We show that on average, our method corrects over six times as many errors as it introduces. We also provide interesting analysis on the relation between scanning quality and other factors such as location and publication year.
A Framework for Learning Assessment through Multimodal Analysis of Reading Behaviour and Language Comprehension
Venue: arXiv cs.CL 2021
Year: 2021

Reading comprehension, which has been defined as gaining an understanding of written text through a process of translating grapheme into meaning, is an important academic skill. Other language learning skills - writing, speaking and listening, all are connected to reading comprehension. There have been several measures proposed by researchers to automate the assessment of comprehension skills for second language (L2) learners, especially English as Second Language (ESL) and English as Foreign Language (EFL) learners. However, current methods measure particular skills without analysing the impact of reading frequency on comprehension skills. In this dissertation, we show how different skills could be measured and scored automatically. We also demonstrate, using example experiments on multiple forms of learners' responses, how frequent reading practices could impact on the variables of multimodal skills (reading pattern, writing, and oral fluency). This thesis comprises of five studies. The first and second studies are based on eye-tracking data collected from EFL readers in repeated reading (RR) sessions. The third and fourth studies are to evaluate free-text summary written by EFL readers in repeated reading sessions. The fifth and last study, described in the sixth chapter of the thesis, is to evaluate recorded oral summaries recited by EFL readers in repeated reading sessions. In a nutshell, through this dissertation, we show that multimodal skills of learners could be assessed to measure their comprehension skills as well as to measure the effect of repeated readings on these skills in the course of time, by finding significant features and by applying machine learning techniques with a combination of statistical models such as LMER.
Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification
Venue: arXiv cs.LG 2021
Year: 2021

Recent years have witnessed the significant success of applying graph neural networks (GNNs) in learning effective node representations for classification. However, current GNNs are mostly built under the balanced data-splitting, which is inconsistent with many real-world networks where the number of training nodes can be extremely imbalanced among the classes. Thus, directly utilizing current GNNs on imbalanced data would generate coarse representations of nodes in minority classes and ultimately compromise the classification performance. This therefore portends the importance of developing effective GNNs for handling imbalanced graph data. In this work, we propose a novel Distance-wise Prototypical Graph Neural Network (DPGNN), which proposes a class prototype-driven training to balance the training loss between majority and minority classes and then leverages distance metric learning to differentiate the contributions of different dimensions of representations and fully encode the relative position of each node to each class prototype. Moreover, we design a new imbalanced label propagation mechanism to derive extra supervision from unlabeled nodes and employ self-supervised learning to smooth representations of adjacent nodes while separating inter-class prototypes. Comprehensive node classification experiments and parameter analysis on multiple networks are conducted and the proposed DPGNN almost always significantly outperforms all other baselines, which demonstrates its effectiveness in imbalanced node classification. The implementation of DPGNN is available at \url{https://github.com/YuWVandy/DPGNN}.
Towards Demystifying Intra-Function Parallelism in Serverless Computing
Venue: arXiv cs.DC 2021
Year: 2021

Serverless computing offers a pay-per-use model with high elasticity and automatic scaling for a wide range of applications. Since cloud providers abstract most of the underlying infrastructure, these services work similarly to black-boxes. As a result, users can influence the resources allocated to their functions, but might not be aware that they have to parallelize them to profit from the additionally allocated virtual CPUs (vCPUs). In this paper, we analyze the impact of parallelization within a single function and container instance for AWS Lambda, Google Cloud Functions (GCF), and Google Cloud Run (GCR). We focus on compute-intensive workloads since they benefit greatly from parallelization. Furthermore, we investigate the correlation between the number of allocated CPU cores and vCPUs in serverless environments. Our results show that the number of available cores to a function/container instance does not always equal the number of allocated vCPUs. By parallelizing serverless workloads, we observed cost savings up to 81% for AWS Lambda, 49% for GCF, and 69.8% for GCR.
Circle Representation for Medical Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Box representation has been extensively used for object detection in computer vision. Such representation is efficacious but not necessarily optimized for biomedical objects (e.g., glomeruli), which play an essential role in renal pathology. In this paper, we propose a simple circle representation for medical object detection and introduce CircleNet, an anchor-free detection framework. Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant. When detecting glomeruli and nuclei on pathological images, the proposed circle representation achieved superior detection performance and be more rotation-invariant, compared with the bounding box. The code has been made publicly available: https://github.com/hrlblab/CircleNet
Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories
Venue: arXiv cs.LG 2021
Year: 2021

Multi-task learning is assumed as a powerful inference method, specifically, where there is a considerable correlation between multiple tasks, predicting them in an unique framework may enhance prediction results. This research challenges this assumption by developing several single-task models to compare their results against multi-task learners to infer mode and purpose of trip from smartphone travel survey data collected as part of a smartphone-based travel survey. GPS trajectory data along with socio-demographics and destination-related characteristics are fed into a multi-input neural network framework to predict two outputs; mode and purpose. We deployed Recurrent Neural Networks (RNN) that are fed by sequential GPS trajectories. To process the socio-demographics and destination-related characteristics, another neural network, with different embedding and dense layers is used in parallel with RNN layers in a multi-input multi-output framework. The results are compared against the single-task learners that classify mode and purpose independently. We also investigate different RNN approaches such as Long-Short Term Memory (LSTM), Gated Recurrent Units (GRU) and Bi-directional Gated Recurrent Units (Bi-GRU). The best multi-task learner was a Bi-GRU model able to classify mode and purpose with an F1-measures of 84.33% and 78.28%, while the best single-task learner to infer mode of transport was a GRU model that achieved an F1-measure of 86.50%, and the best single-task Bi-GRU purpose detection model that reached an F1-measure of 77.38%. While there's an assumption of higher performance of multi-task over sing-task learners, the results of this study does not hold such an assumption and shows, in the context of mode and trip purpose inference from GPS trajectory data, a multi-task learning approach does not bring any considerable advantage over single-task learners.
Group-disentangled Representation Learning with Weakly-Supervised Regularization
Venue: arXiv cs.LG 2021
Year: 2021

Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning. We investigate learning group-disentangled representations for groups of factors with weak supervision. Existing techniques to address this challenge merely constrain the approximate posterior by averaging over observations of a shared group. As a result, observations with a common set of variations are encoded to distinct latent representations, reducing their capacity to disentangle and generalize to downstream tasks. In contrast to previous works, we propose GroupVAE, a simple yet effective Kullback-Leibler (KL) divergence-based regularization across shared latent representations to enforce consistent and disentangled representations. We conduct a thorough evaluation and demonstrate that our GroupVAE significantly improves group disentanglement. Further, we demonstrate that learning group-disentangled representations improve upon downstream tasks, including fair classification and 3D shape-related tasks such as reconstruction, classification, and transfer learning, and is competitive to supervised methods.